Comparing ANNs and Genetic Programming for Voice Quality Assessment Post-Treatment

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  title =        "Comparing {ANNs} and Genetic Programming for Voice
                 Quality Assessment Post-Treatment",
  author =       "Tim Ritchings and Carl Berry and Walaa Sheta",
  journal =      "Applied Artificial Intelligence",
  year =         "2008",
  number =       "3",
  volume =       "22",
  bibdate =      "2008-12-11",
  bibsource =    "DBLP,
  pages =        "198--207",
  DOI =          "doi:10.1080/08839510701734343",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In the U.K., the rehabilitation of a patient's voice
                 following treatment for cancer of the larynx is managed
                 by Speech and Language Therapists (SALT), who listen to
                 a patient's stylized speech and then use their
                 experience and domain knowledge to make an assessment
                 of the current quality of the patient's voice. This
                 process is very subjective and time consuming, and
                 could benefit from using AI techniques to provide
                 objective, reproducible assessments of voice quality. A
                 comparative study of voice quality assessment
                 post-treatment using Artificial Neural Networks (ANN),
                 the preferred AI technique in this application area,
                 and Genetic Programming (GP) is described, using the
                 same dataset, training, and verification procedures.
                 The GP approach was found to give more accurate
                 classifications of bad quality (immediately
                 post-treatment) and good quality (recovered) voicings
                 than the ANN, and in addition, gave indication of the
                 most significant parameters in the input dataset.",

Genetic Programming entries for Tim Ritchings Carl Berry Walaa Sheta